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The way ML people use "dimension", each free parameter is an extra dimension. A high-resolution 2D image is considered to have millions of dimensions - one for
by Legend2440 3y ago
The way ML people use "dimension", each free parameter is an extra dimension. A high-resolution 2D image is considered to have millions of dimensions - one for each pixel.
- atoav 3y agoAnd each pixel is what, a RGB-vector?
- dartos 3y agoIn the context of neural networks, kind of. It’s 3 numbers on the input layer, but that could influence N parameters in later layers.
- PartiallyTyped 3y agoWe usually do HxWxC, for height, width, and channels, so each pixel is addressed via the two first dims of the input, and then it has 3 channels. Of course, you can transpose the tensor to CxHxW or CxWxH. Different ordering behaves differently with respect to memory locality.
- polynomial 3y agoYes, each pixel is a vector of the rasterized vector space.
- hommelix 3y agoThanks for clarifying the dimensions.
- reedf1 3y agoThese are all expressions of the same concept. The confusion is that physicists are describing the dimensionality of specific systems (space, spacetime, superstring theory, supergravity) - that doesn't mean that this limits the dimensionality of other systems (which is often where confusion lies between laymen).
- tnecniv 3y agoThat’s not just ML, that’s linear algebra. Moreover, across fields in math, the dimension of an object is defined differently, but it’s always a very fundamental property of an object that captures loosely how complex an object is. Often, that complexity is related to how many numbers you need to describe said object.